Effect of Filling Humidity on the Propagation of High-Amplitude Stress Waves through an Artificial Joint
Bibliographic record
Abstract
Abstract The purpose of this article is to further investigate the seismic response of an artificial filled joint under high-amplitude stress waves considering the effect of filling humidity, following our earlier work on dry infill. A steel split Hopkinson pressure bar system is utilized to induce high-amplitude stress waves to the filled joint. In this study, the wet infill is a mixture composed of quartz sand, kaolinite clay, and water. It is found that when the water content is relatively low, i.e., 8.25 %, the seismic response of the joint with wet infill is similar to that of the joint with dry infill, as shown in the literature. When the stress wave amplitude increases, the infill is progressively compacted and the transmission coefficient increases. However, there exists a crushing deformation stage for the infill in which many particles are crushed, and the transmission coefficient decreases as the incident wave amplitude increases. The water in the infill can reduce the friction between grains, which may lead to the decrease of the joint stiffness. As a result, the transmission coefficient is smaller than the case with dry infill under similar loading conditions. When the water content is moderate, such as 16.75 %, particles are very difficult to crush and the infill dominantly experiences compaction even when loaded by very high-amplitude stress waves. Consequently, the transmission coefficient through the wet infill always increases with the increase of the incident wave amplitude. When the infill is fully saturated (water content = 25.0 %), it can only experience approximately elastic deformation, and few particles can be crushed. In this case, the transmission coefficient is independent of the incident wave amplitude. When the infill is dry or fully saturated, the transmission coefficient is insensitive to the amplitude of the incident wave.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".